A key characteristic of TSGB is that it is task-level objective-oriented while training all the tasks in the same trees in a homogeneous MTL setting, which makes TSGB easy to train and elegant in MTL. The empirical results also show the effectiveness of TSGB.
Previous works either ignore the task-specific objective by simply splitting the tasks with pre-defined task features on randomly selected leaf nodes [1]} or train both task-common trees and task-specific trees at the same time. The former can not make full use of the correlation of different task data, while the latter always derive a huge redundant model since \(T+1\)  forests are needed [2]}.
